Summer of Code Project Ideas

The following is distilled from the Projects page for the benefit of potential Google and ESA Summer of Code (SoC) students. Although students are welcome to attempt any of the projects in that page or any of their own choosing, here we offer some suggestions on what good student projects might be.

You can also take a look at last years Summer of Code projects for inspiration.

If you aren't communicating with us before the application is due, your application will not be accepted.

Join the maintainers mailing list or read the archives and see what topics we discuss and how the developers interact with each other.

Hang out in our IRC channel. Ask questions, answer questions from users, show us that you are motivated, and well-prepared. There will be more applicants than we can effectively mentor, so do ask for feedback on your public application to increase the strength of your proposal!

Do not wait for us to tell you what to do

You should be doing something that interests you, and should not need us to tell you what to do. Similarly, you shouldn't ask us what to do either.

When you email the list and mentors, do not write it to say in what project you're interested. Be specific about your questions and clear on the email subject. For example, do not write an email with the subject "GSoC student interested in the ND images projects". Such email is likely be ignored. Instead, show you are already working on the topic, and email "Problem implementing morphological operators with bitpacked ND images".

It is good to ask advice on how to solve something you can't but you must show some work done. Remember, we are mentors and not your boss. Read How to ask questions the smart way:

Prepare your question. Think it through. Hasty-sounding questions get hasty answers, or none at all. The more you do to demonstrate that having put thought and effort into solving your problem before seeking help, the more likely you are to actually get help.

It can be difficult at the beginning to think on something to do. This is nature of free and open source software development. You will need to break the mental barrier that prevents you from thinking on what can be done. Once you do that, you will have no lack of ideas for what to do next.

Use Octave. Eventually you will come across something that does not work the way you like. Fix that. Or you will come across a missing function. Implement it. It may be a hard problem (they usually are). While solving that problem, you may find other missing capabilities or smaller bug fixes. Implement and contribute those to Octave.

Take a look at the Short projects for something that may be simple to start with.

It's critical that you find a project that excites you. You'll be spending most of the summer working on it (we expect you to treat the SoC as a full-time job).

Don't just tell us how interested you are, show us that you're willing and able to contribute to Octave. You can do that by fixing a few bugs or submitting patches well before the deadline, in addition to regularly interacting with Octave maintainers and users on the mailing list and IRC. Our experience shows us that successful SoC students demonstrate their interest early and often.

Octave is mostly written in C++ and its own scripting language that is mostly compatible with Matlab. There are bits and pieces of Fortran, Perl, C, awk, and Unix shell scripts here and there. In addition to being familiar with C++ and Octave's scripting language, successful applicants will be familiar with or able to quickly learn about Octave's infrastructure. You can't spend the whole summer learning how to build Octave or prepare a changeset and still successfully complete your project.

Mercurial is the distributed version control system (DVCS) we use for managing our source code. You should have some basic understanding of how a DVCS works, but hg is pretty easy to pick up, especially if you already know a VCS like git or svn.

The Procedure for Contributing Changesets

You will be expected to follow the same procedures as other contributors and core developers.

You will be helping current and future Octave developers by using our standard style for changes, commit messages, and so on. You should also read the same contributionguidelines we have for everyone.

This page describes the procedures students are expected to use to publicly display their progress in a public mercurial repo during their work.

These might vary somewhat depending on the mentors and coordinators for a particular Summer of Code, but typically the main factors considered would be:

Applicant has demonstrated an ability to make substantial modifications to Octave

The most important thing is that you've contributed some interesting code samples to judge you by. It's OK during the application period to ask for help on how to format these code samples, which normally are Mercurial patches.

Applicant shows understanding of topic

Your application should make it clear that you're reasonably well versed in the subject area and won't need all summer just to read up on it.

Applicant shows understanding of and interest in Octave development

The best evidence for this is previous contributions and interactions.

Well thought out, adequately detailed, realistic project plan

"I'm good at this, so trust me" isn't enough. You should describe which algorithms you'll use and how you'll integrate with existing Octave code. You should also prepare a full timeline and goals for the midterm and final evaluations.

The following projects are broadly grouped by category and probable skills required to tackle each. Remember to check Projects for more ideas if none of these suit you, and your own ideas are always welcome.

Note: these are suggested projects but you are welcome to propose your own projects provided you find an Octave mentor

An initial implementation of a Matlab compatible ode15{i,s} solver,
based on SUNDIALS,
was done by Francesco Faccio during
GSOC 2016.
The blog describing the work is here.
The resulting code has been pushed into the main Octave repository in the development branch and
consists mainly of the following three files
__ode15__.cc,
ode15i.m and
ode15s.m.
The list of outstanding tracker tickets concerning this implementation can be found
here

Possible useful improvements that could be done in a new project include:

Implement a better function for selecting consistent initial conditions compatible with Matlab's decic.m. The algorithm to use is described here

make ode15{i,s} with datatypes other than double

improve interpolation at intermediate time steps.

general code profiling and optimization

Other tasks, not strictly connected to ode15{i,s} but closely related that could be added
to a possible project plan would be improving documentation and tests in odepkg and removing
overlaps with the documentation in core Octave.

GNU Octave currently has the following Krylov subspace methods for sparse linear systems: pcg (spd matrices) and pcr (Hermitian matrices), bicg,
bicgstab, cgs, gmres, and qmr (general matrices). The description of some of them (pcr, qmr) and their error messages are not aligned. Moreover, they have similar blocks of code (input check for instance) which can be written once and for all in common functions. The first step in this project could be a revision and a synchronization of the codes, starting from the project SOCIS2016, whose latest patch, still to be included, is here.

In Matlab, some additional methods are available: minres and symmlq (symmetric matrices), bicgstabl (general matrices), lsqr (least
squares). The second step in this project could be the implementation of some of these missing functions.

The reference book is available [www-users.cs.umn.edu/~saad/IterMethBook_2ndEd.pdf here]

Chebfun is a mathematics and software project for "numerical computing with functions". Basically it approximates functions to machine precision accuracy (10-15) using piecewise Chebyshev polynomial interpolants. Operations on those functions (arithmetic, derivatives, root-finding, etc) are then overloaded and return new interpolating polynomials, which are themselves proxies for the actual solution.

Chebfun makes extensive use of classdef classes, and is one of the largest Free Software projects to do so. Unfortunately it currently only works in Matlab. This project seeks to (1) improve Octave's classdef support and (2) tweak Chebfun to work under Octave, for example, removing undocumented classdef features. The final goal is to have at least basic Chebfun features working on Octave. An additional goal would be making "pkg install chebfun.zip" work in Octave.

The impact of this project is improving Octave and allowing Chebfun to be used without proprietary software.

How to get started: learn about Chebfun, browse Octave's bug list for classdef-related bugs.

First steps: clone this Chebfun octave_dev branch. On that, "f = chebfun(@(x) sin(x), [-2 6])" should work with Octave 4.3.0+ and maybe even with 4.2.1. Check that "f(pi)" and "g = f + 1" work. A good first task would be to study this commit, a workaround for "f.funs{1}" using "temp = f.funs; temp{1}". "2*f" is failing, can you fix it, perhaps with this workaround? Or can you make "f.funs{1}" work by changing something in "@chebfun/subsref.m"?

Octave's Symbolic package handles symbolic computing and other CAS tools. The main component of Symbolic is a pure m-file class "@sym" which uses the Python package SymPy to do (most of) the actual computations. The package aims to expose the full functionality of SymPy while also providing a high-level of compatibility with the Matlab Symbolic Math Toolbox. The Symbolic package requires communication between Octave and Python. Recently, a GSoC2016 project successfully re-implemented this communication using the new Pytave tool.

This project proposes to go further: instead of using Pytave only for the communication layer, we'll use it throughout the Symbolic project. For example, we might make "@sym" a subclass of "@pyobject". We also could stop using the "python_cmd" interface and use Pytave directly from methods. The main goal was already mentioned: to expose the *full functionality* of SymPy. For example, we would allow OO-style method calls such as "f.diff(x)" instead of "diff(f, x)".

The interval package provides several arithmetic functions with accurate and guaranteed error bounds. Its development started in the end of 2014 and there is some fundamental functionality left to be implemented. See the list of functions, basically any missing numeric Octave function could be implemented as an interval extension in the package. Potential projects:

Implement missing algorithms (as m-files)-difficulty and whether knowledge in interval analysis is required depends on the particular function. Of course, you may use papers which present such algorithms.

Improve existing algorithms (support more options for plotting, support more options for optimizers, increase accuracy, …)

Integrate functions from VERSOFT [1] in the package (some work has already been done and current progress is tracked in Interval_package#VERSOFT). This basically involves conversion of the documentation into Texinfo format, use Octave coding guidelines and to make sure that any called functions are available in the interval package. VERSOFT is originally based on INTLAB [2], a proprietary Octave/Matlab package. Some functions may be missing. Also, the interval package doesn't support complex numbers, so it might not be possible to migrate some functions.

List more interesting use cases of interval arithmetic in the package's manual [3]

OCS is a circuit simulator for Octave. The objective of this project is to update the code to use modern features of Octave (e.g. classdef), fix open bugs, increase compatibility with SPICE and improve compatibility with other Octave packages (odepkg, control etc).

Jupyter Notebook is a web-based worksheet interface for computing. There is a Octave kernel for Jupyter. This project seeks to improve that kernel to make Octave a first-class experience within the Jupyter Notebook.

Pytave allows one to call Python functions and interact with Python objects from within Octave .m file code and from the Octave command line interface. Ideally, Pytave will not be a separate project, but rather a core feature of Octave. This project aims to improve Pytave with the goal of merging the code into the core Octave code base.

Based on a previous summer project related to Pytave, this work will consist of fast-paced collaborative software development based on tackling the pytave issue list. You would also be expected to participate in software design decisions and discussion, as well as improve documentation, doctests and unit tests. As an example of the sorts of decision decisions being made, note that Octave indexes from 1 whereas Python typically indexes from 0; in which cases is it appropriate to make this transparent to the user?

Packages are extensions for Octave, that are mainly maintained by the Octave Forge community.
To get those extension to work with Octave, there is a single function, pkg, which does pretty much everything.
This function has a few limitations which are hard to implement with the current codebase, and will most likely require a full rewrite.
A major step forward for a rewritten package manager is the "packajoozle" project by Andrew Janke.

The main objective of this project is to make pkg more user friendly and to make it a tool to foster third party participation in Octave.
However, the current pkg also performs some maintenance functions which it probably should not.
Instead a package for developers should be created with such tools.
To do this enhancement effectively, a refactoring of the current pkg code will be needed (see "packajoozle" project).

Many of these problems have been solved in other languages.
Familiarity with how other languages handle this problem will be useful to come up with elegant solutions.
In some cases, there are standards to follow.
For example, there are specifications published by freedesktop.org about where files should go (base directory spec) and Windows seems to have its own standards.
See bugs #36477 and #40444 for more details.

In addition, package names may start to collide very easily.
One horrible way to workaround this by is choosing increasingly complex package names that give no hint on the package purpose.
A much better is option is providing an Authority category like Perl 6 does.
Nested packages is also an easy way to provide packages for specialized subjects (think image::morphology).
A new pkg would think all this things now, or allow their implementation at a later time.
Read the unfinished plan for more details.

Minimum requirements

Ability to read and write Octave code, experience with Octave packages, and understanding of the basics of autotools. The most important skill is software design.

The image package has partial functionality for N-dimensional images. These images exist for example in medical imaging where slices from scans are assembled to form anatomical 3D images. If taken over time and at different laser wavelengths or light filters, they can also result in 5D images. Albeit less common, images with even more dimensions also exist. However, their existence is irrelevant since most of the image processing operations are mathematical operations which are independent of the number of dimensions.

As part of GSoC 2013, the core functions for image IO, imwrite and imread, were extended to better support this type of images. Likewise, many functions in the image package, mostly morphology operators, were expanded to deal with this type of image. Since then, many other functions have been improved, sometimes completely rewritten, to abstract from the number of dimensions. In a certain way, supporting ND images is also related to choosing good algorithms since such large images tend to be quite large.

This project will continue on the previous work, and be mentored by the previous GSoC student and current image package maintainer. Planning the project requires selection of functions lacking ND support and identifying their dependencies. For example, supporting imclose and imopen was better implemented by supporting imerode and imdilate which then propagated ND support to all of its dependencies. These dependencies need to be discovered first since often they are not being used yet, and may even be missing function. This project can also be about implementing functions that have not yet been implemented. Also note that while some functions in the image package will accept ND images as input, they are actually not correctly implemented and will give incorrect results.

Required skills

m-file scripting, and a fair amount of C++ since a lot of image analysis cannot be vectorized. Familiarity with common CS algorithms and willingness to read literature describing new algorithms will be useful.

There are a lot of image formats. To handle this, Octave uses GraphicsMagic (GM), a library capable of handling a lot of them in a single C++ interface. However, GraphicsMagick still has its limitations. The most important are:

GM has build option quantum which defines the bitdepth to use when reading an image. Building GM with high quantum means that images of smaller bitdepth will take a lot more memory when reading, but building it too low will make it impossible to read images of higher bitdepth. It also means that the image needs to always be rescaled to the correct range.

GM supports unsigned integers only thus incorrectly reading files such as TIFF with floating point data

GM hides away details of the image such as whether the image file is indexed. This makes it hard to access the real data stored on file.

This project would implement better image IO for scientific file formats while leaving GM handle the others. Since TIFF is the de facto standard for scientific images, this should be done first. Among the targets for the project are:

implement the Tiff class which is a wrap around libtiff, using classdef. To avoid creating too many private __oct functions, this project could also create a C++ interface to declare new Octave classdef functions.

improve imread, imwrite, and imfinfo for tiff files using the newly created Tiff class

port the bioformats into Octave and prepare a package for it

investigate other image IO libraries

clean up and finish the dicom package to include into Octave core

prepare a matlab compatible implementation of the FITS package for inclusion in Octave core

Required skills

Knowledge of C++ and C since most libraries are written in those languages.

Octave has a preliminary implementation of a Variable Editor: a spreadsheet-like tool for quickly editing and visualizing variables. The initial phase of the project will be learning how the implementation was done.

With the knowledge gained, the second part of the project will be to implement a Property Inspector. This is a spreadsheet like interface to the many, many graphics properties that exist and are different on a per-object basis. The goal would be not only the concise-display of the existing properties, but a reasonable user interface to change them. As examples, boolean properties should be able to be toggled with a double-click; Radio properties should have a drop-down list of only the supported options; Other properties that can be modified should have the constraints built-in (for example, Linewidth must be a scalar, while Position must be a 1x4 vector). It would also be important to have easy access to the documentation of a property.

Octave currently provides supports for polar axes by using a Cartesian 2-D axes and adding a significant number of properties and callback listerners to get things to work. What is needed is a first class implementation of a "polaraxes" object in C++. This will require creating a new fundamental graphics object type, and programming in C++/OpenGL to render the object. When "polaraxes" exist as an object type then m-files will be written to access them including polaraxes.m, polarplot.m, rticks.m, rticklabels.m, thetaticks, thetaticklabels.m, rlim.m, thetalim.m. relates to #35565, #49804, #52643.

Minimum requirements

Ability to read and write C++ code. Ability to read and write Octave code. Experience with OpenGL programming is optional.